In generative modeling, numerous successful approaches leverage a low-dimensional
latent space, eg, Stable Diffusion models the latent space induced by an encoder and …

The rank of neural networks measures information flowing across layers. It is an instance of
a key structural condition that applies across broad domains of machine learning. In …

We propose a novel pipeline for the generation of synthetic ultrasound images via
Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps …

As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs)
face a critical challenge in controllability, struggling to adhere strictly to complex, multi …

Abstract Training Generative adversarial networks (GANs) stably is a challenging task. The
generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data …

In this paper, we propose leveraging causal generative learning as an interpretable tool for
explaining image classifiers. Specifically, we present a generative counterfactual inference …

As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs)
face a critical challenge in controllability, struggling to adhere strictly to complex, multi …

We study the problem of reconstructing small objects from their low-resolution images, by
modelling them as r-regular objects. Previous work shows how the boundary constraints …